DESCRIPTION OVERALL: (provided by applicant): One of the greatest challenges in animal biology is to learn how genomic sequence information is read by transcription factors to produce patterns of gene expression within the context of regulatory networks in developing embryos. This proposed Program Project will integrate computational modeling and wet laboratory methods to address this challenge in the belief that only quantitative, predictive mathematical models that have been validated experimentally can provide the rigorous understanding required. The proposal builds on a set of complementary, quantitative datasets that we have established for the Drosophila early embryo regulatory network, together with initial computational models for the targeting of factors to DNA and for the subsequent generation of specific patterns of transcriptional output. These preliminary experiments illustrate that factors show a shockingly broad, quantitative continuum of binding and function to highly overlapping genomic regions in vivo and suggest the molecular mechanism chiefly responsible for driving DNA binding in vivo. Our proposal is organized into four interdependent Research Projects and one Shared Resource Core. These will map at a new, much higher resolution the binding of transcription factors to their specific recognition sites in embryos; test the predictions of our computational models by extensively measuring the effect of point mutations in factor recognition sites on both in vivo factor occupancy and spatial and temporal transcriptional outputs; establish image analysis methods to measure relative rates of nuclear transcription cell by cell; and develop an ordered series of computational models that link input and output datasets to establish the key molecular interactions within a transcription network and grammar rules for the organization of functional factor recognition sites. Our project will provide uniquely detailed datasets and modeling strategies for studying the developmental control of transcription, including extensive experimental testing and validation of the models predictions.